Preučevanje gozdnih sestojev in posameznih drevesnih vrst iz normalizirane intenzitete laserskih točk : magistrsko delo št.: 114/II. GIG

Kranjec, Nina (author), Triglav Čekada, Mihaela (mentor), Kobal, Milan (comentor)

V magistrski nalogi smo preučevali nenormalizirane in normalizirane intenzitete odbojev laserskih žarkov na izbranih gozdnih sestojih in posameznih drevesnih krošnjah izbranih drevesnih vrst na testnem območju Zelenice. Izbrano območje je bilo lasersko skenirano v štirih aerolaserskih podatkovnih nizih posnetih v dveh valovnih dolžinah (1064 nm in 1550 nm) ter v različnih delih leta (marec, maj, julij in september). Vrednosti intenzitet smo najprej analizirali na naključno izbranih vrhovih drevesnih krošenj na območju gozda, kjer smo opazovali vrednosti intenzitet odboja glede na vrstno sestavo (iglasti, iglasti z listavci, listnati z iglavci ali listnati gozd). V nadaljevanju smo na podlagi terenskega ogleda, ortofota in digitalnega model krošenj izbrali po 40 dreves šestih različnih drevesnih vrst: smreka, bor, macesen, bukev, javor in jesen. V analizi smo upoštevali točke vrha krošnje (zgornjih 3 m drevesnih krošenj). Najprej smo drevesne vrste primerjali na podlagi geometrije vrha krošnje tako, da smo med seboj primerjali površine prerezov, površine tlorisov in volumnov. Nato smo drevesne vrste primerjali na podlagi normaliziranih in nenormaliziranih intenzitet točk vrha krošenj. Statistično značilno smo dokazali razlike v intenziteti točk med posameznimi drevesnimi vrstami. Izdelali smo model napovedovanja posameznih drevesnih vrst iz laserskih podatkov s pomočjo odločitvenega drevesa. Z odločitvenim drevesom smo uspešno prepoznavali drevesa kot iglasta ali listnata (uspešnost modela 95 %), uspešno pa smo prepoznali tudi izbrane drevesne vrste (uspešnost modela 60 %).

• Language: Slovenian
• Keywords: geodezija, magistrska dela, GIG, aerolasersko skeniranje, intenziteta, geometrija krošnje, vrste dreves, odločitveno drevo
• Work type: Master's thesis/paper
• Typology: 2.09 - Master's Thesis
• Organization: FGG - Faculty of Civil and Geodetic Engineering
• Place of publishing: Ljubljana
• Publisher: [N. Kranjec]
• Year: 2020
• Number of pages: X, 57 str. 6 str. pril.
• PID: 20.500.12556/RUL-120607
• UDC: 528.715:633/635.055(497.4)(043.3)
• COBISS.SI-ID: 37600259
• Publication date in RUL: 23.09.2020

KRANJEC, Nina, 2020, Preučevanje gozdnih sestojev in posameznih drevesnih vrst iz normalizirane intenzitete laserskih točk : magistrsko delo št.: 114/II. GIG [online]. Master’s thesis. Ljubljana : N. Kranjec. [Accessed 17 May 2025]. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=120607

Secondary language

• Language: English
• Title: Investigation of forest stands and individual tree species from normalized intensity of laser points : master thesis no.: 114/II. GIG

Abstract

In this master's thesis, we studied non-normalized and normalized intensities of aerial laser scanning on selected forest stands and individual tree canopies in the test area of the Zelenica, Slovenia. The selected area was aerial laser scanned by four data sets recorded in two wavelengths (1064 nm and 1550 nm) and in different parts of the year (March, May, July and September). Intensity values were first analysed at randomly selected tree canopy peaks in the forest area, where we analysed values of reflected intensities according to species composition (coniferous, coniferous with deciduous, deciduous with coniferous or deciduous forest). Based on a field trip, orthophoto and digital canopy model, we selected 40 individual trees for six different tree species: spruce, pine, larch, beech, maple and ash. Only the points of the upper 3 m of tree canopies were considered in the analysis. First, a comparison was made of tree canopies based on geometry by comparing cross-sectional areas, floor areas and volumes. Secondary, a comparison of tree species based on normalized and non-normalized intensities was performed. Further, we have statistically significantly assessed the differences between the characteristics of intensities of individual tree species. We developed a model for predicting individual tree species from aerial laser data using a decision tree algorithm. Using the decision tree algorithm, we successfully identified trees as coniferous or deciduous (model accuracy 95%), and we also successfully identified selected tree species (model accuracy 60%).

• Keywords: geodesy, master thesis, aerial laser scanning, intensity, canopy geometry, tree species, decision tree